The following discussion of the prior art is intended to present the invention in an appropriate technical context and allow its significance to be properly appreciated. Unless clearly indicated to the contrary, however, reference to any prior art in this specification should not be construed as an admission that such art is widely known or forms part of common general knowledge in the field.
Nanopositioning systems and apparatuses are used to generate fine mechanical displacements with resolution frequently down to the atomic scale. Such systems and apparatuses include fiber aligners, beam scanners, and lateral positioning platforms. Other applications of nanopositioning apparatuses in nanotechnology include scanning probe microscopy (SPM), scanning tunnelling microscopy (STM), atomic force microscopy (AFM), nanofabrication systems, precision machining, optical switching and cell physiology research.
Due to their high stiffness, compact size and effectively infinite resolution, piezoelectric actuators are universally employed in nanopositioning applications to provide the greatest possible positioning accuracy, which is also known as tracking performance. In practice, however, the positioning accuracy of piezoelectric actuators is severely limited by hysteresis over relatively large displacements and creep at low frequencies. Hysteresis occurs where the platform position becomes a function of the past history of its movement. This is due to the piezoelectric response to an input voltage being a function of the previous voltage history. Creep occurs when the platform slowly drifts in the direction of recent movements. These slow drifts in position occur due to previous input voltages applied to the nanopositioning apparatus. As a result, all nanopositioning systems typically require some form of feedback or feedforward control to reduce non-linearity caused by hysteresis and creep.
Another difficulty with nanopositioning systems is mechanical resonance, which arises from the platform mass interacting with the finite stiffness of the support flexures, mechanical linkages and/or actuator. Mechanical resonance introduces unwanted vibrations into the nanopositioning system, thus affecting its positioning accuracy, scanning speed and stability. Thus, the frequency of driving signals is limited to around 1% to 10% of the resonance frequency. This necessarily restricts the closed-loop bandwidth of the nanopositioning system.
One of the most popular techniques for control of nanopositioning systems is sensor-based feedback using integral or proportional integral control. However, the bandwidth of integral tracking control is severely limited by the presence of highly resonant modes and sensor-induced noise. It has been proposed to improve the closed-loop bandwidth by using either inversion of resonance dynamics with a notch filter or suspension of resonance dynamics using a damping controller.
However, each of these proposed solutions suffer drawbacks. Inversion based techniques suffer from the disadvantage of requiring an accurate system model. For example, if the resonance frequency of the system shifts by only 1%, a high-gain inversion based feedback controller can become unstable. In most applications this is unacceptable as the load mass and hence resonance frequency of a nanopositioning apparatus can vary significantly during service. As a result of this sensitivity, high-performance inversion based controllers are typically only applied in niche applications where the resonance frequency is stable, or when the feedback controller can be continually recalibrated.
Attempts to improve the performance of nanopositioning systems and methods frequently utilise damping techniques to actively damp the first resonance mode. This can reduce settling time, allows a proportional increase in scan speed and facilitates greater tracking performance, since the tracking controller gain can be increased.
Damping controllers are adequate to reduce the bandwidth limitations caused by mechanical resonance, but the tracking controller gain is still limited by stability margins and positioning accuracy remains dominated by sensor-induced noise.